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import streamlit as st
import backtrader as bt
import yfinance as yf
import matplotlib.pyplot as plt
from datetime import datetime
# Define the trading strategy
class SmaCross(bt.Strategy):
params = dict(
pfast=10, # period for the fast moving average
pslow=30 # period for the slow moving average
)
def __init__(self):
sma1 = bt.ind.SMA(period=self.p.pfast) # fast moving average
sma2 = bt.ind.SMA(period=self.p.pslow) # slow moving average
self.crossover = bt.ind.CrossOver(sma1, sma2) # crossover signal
def next(self):
if not self.position: # not in the market
if self.crossover > 0: # if fast crosses slow to the upside
self.buy() # enter long
elif self.crossover < 0: # in the market & cross to the downside
self.close() # close long position
# Function to run backtest and return cerebro
def run_backtest(data):
cerebro = bt.Cerebro()
cerebro.addstrategy(SmaCross)
feed = bt.feeds.PandasData(dataname=data)
cerebro.adddata(feed)
cerebro.broker.setcash(10000)
cerebro.addsizer(bt.sizers.AllInSizer, percents=95) # Use 95% of the portfolio for each trade
cerebro.run()
return cerebro
# Initialize session state for running status
if 'running' not in st.session_state:
st.session_state.running = False
# Streamlit app layout
st.title('Cryptocurrency Trading Bot Simulation')
# container for user input
st.header('User Input Parameters')
selected_crypto = st.selectbox('Select cryptocurrency', ('BTC-USD', 'ETH-USD', 'LTC-USD'))
start_date = st.date_input('Start date', datetime(2020, 1, 1))
end_date = st.date_input('End date', datetime(2021, 1, 1))
# Function to handle start button
def start_bot():
st.session_state.running = True
# Function to handle stop button
def stop_bot():
st.session_state.running = False
# Buttons to start/stop the bot
st.button('Start Bot', on_click=start_bot)
st.button('Stop Bot', on_click=stop_bot)
# Display bot status
if st.session_state.running:
st.success('Bot is running!')
# Fetch historical data from yfinance
data = yf.download(selected_crypto, start=start_date, end=end_date)
# Check if data was successfully fetched
if not data.empty:
# Run backtest
cerebro = run_backtest(data)
# Show results
st.header('Simulation Results')
st.write(f'Final Portfolio Value: ${cerebro.broker.getvalue():,.2f}')
# Plot the results manually using matplotlib
fig, ax = plt.subplots()
for data in cerebro.datas:
ax.plot(data.datetime.array, data.close.array, label='Close')
ax.legend()
# Show the plot
st.pyplot(fig)
else:
st.error('Bot is stopped!') |